The work is devoted to the development of software for automated detection of potentially dangerous objects in the form of large bags/backpacks on video streams at the entrance of participants to mass events. A client-server solution architecture is proposed, which includes a Streamlit interface, an Ultralytics YOLOv11 detection module, and an incident registration and alerting subsystem. The accuracy of bag identification is verified using the formalized “coordinated detection” criterion: an event is recorded when the confidence threshold γ is exceeded on at least α frames in a sliding window of length τ. On four RTSP channels (processing every 3rd frame), latency of ≈0.5 s was achieved, and false positives were mitigated through time aggregation. The YOLOv11 neural network was trained on a dataset containing 36,000 images, resulting in stable performance (P ≈ 0.953 for “dangerous” object detection accuracy and R ≈ 0.896 for completeness/sensitivity).